A self-location estimation device includes a map information acquisition unit, an environmental information acquisition unit, and a self-location estimation unit. The map information acquisition unit is configured to acquire map information in a storage facility generated based on shape information of an object and storage status information of the object in the storage facility. The environmental information acquisition unit is configured to acquire environmental information of surroundings. The self-location estimation unit is configured to estimate a self-location based on the map information acquired by the map information acquisition unit and the environmental information acquired by the environmental information acquisition unit.

A vehicle includes an electric chassis provided with a navigation device and a vehicle body to be connected to the electric chassis. The navigation device is configured to control the electric chassis to travel according to a predetermined path. A vehicle final assembly line includes: a vehicle body positioning device, configured to position the vehicle body on the electric chassis to connect the vehicle body to the electric chassis at a marriage station when the navigation device controls the electric chassis to travel to the marriage station according to the predetermined path, and control the vehicle body and the electric chassis that are connected to each other to travel to a plurality of assembly stations in sequence according to the predetermined path so as to complete assembly of the vehicle at the plurality of assembly stations, and the assembled vehicle leaves the line at an end-of-line station.

A system used in a factory for manufacturing a vehicle includes: a remote control unit remotely controlling a vehicle capable of running in a factory in a manufacturing process at the factory, the vehicle including a communication device having a communication function for remote control and a secondary battery for running; a state-of-charge acquisition unit acquiring a state of charge of the secondary battery; and a state-of-charge adjustment determination unit acquiring a target value of the state of charge and determining whether or not to adjust the state of charge using the acquired target value and a current state of charge. When the state-of-charge adjustment determination unit determines that the state of charge is to be adjusted, the remote control unit executes a state-of-charge adjustment process by the remote control of the vehicle, thereby bringing the state of charge closer to the target value, wherein the state-of-charge adjustment process includes at least one of a discharging process for discharging the secondary battery and a charging process for charging the secondary battery.

A system used in a factory for manufacture of a vehicle comprises: a remote controller remotely controlling running of the vehicle capable of running in the factory during a course of manufacture in the factory, the vehicle including a communication device having a communication function for remote control and a secondary battery for running; a power feeder available for charging the secondary battery; a manufacturing status acquisition unit acquiring information about a manufacturing status in the factory; and a feeding judgment unit judging whether to feed the vehicle using the acquired information about the manufacturing status. When the feeding judgment unit judges that the vehicle is to be fed, the vehicle is fed using the power feeder.

In conventional robot navigation techniques learning and planning algorithms act independently without guiding each other simultaneously. A method and system for robotic navigation with simultaneous local path planning and learning is disclosed. The method discloses an approach to learn and plan simultaneously by assisting each other and improve the overall system performance. The planner acts as an actuator and helps to balance exploration and exploitation in the learning algorithm. The synergy between dynamic window approach (DWA) as a planning algorithm and a disclosed Next best Q-learning (NBQ) as a learning algorithm offers an efficient local planning algorithm. Unlike the traditional Q-learning, dimension of Q-tree in the NBQ is dynamic and does not require to define a priori.

A system comprises: a remote controller that causes a vehicle to run by remote control, the vehicle being capable of running along a track in a factory during a course of manufacture, the vehicle including a vehicle communication unit and a driving controller, the vehicle communication unit having a communication function, the driving controller implementing driving control over the vehicle; a track information acquisition unit that acquires track information that is information about an environment of a track on which the vehicle is configured to run by the remote control; and a running method determination unit that determines a running method using the acquired track information, the running method including at least one of a possibility or impossibility of running of the vehicle and a running route along which the vehicle is to run.

A system and methods for operating a multi-robot system (MRS) are disclosed. An example method can include receiving at least one transportation task; determining an optimal path for executing the at least one transportation task based at least in part on: (i) one or more transportation task parameters, (ii) a shared global critic function accessible to the first robot and the at least one additional robot, and (iii) a local critic function unique to the first robot; and executing the at least one transportation task in accordance with the determined optimal path.

A control system for controlling transport of a vehicle in any of steps from production to shipment of the vehicle includes a state identification unit that identifies a vehicle state that is a state of the vehicle, and a control notification unit that determines a control content of the vehicle by using the identified vehicle state and notifies the vehicle of the control content. The control notification unit determines to control at least either one of a transport route of the vehicle and a timing of starting the transport as the control content such that a magnitude of a difference between the vehicle state and a preset target state is suppressed.

A mobile autonomous manufacturing system including an autonomously mobile main device (10) performing a manufacturing task, a first auxiliary device (11) adapted to autonomously supply energy to the main device (10), a second auxiliary device (12), adapted to autonomously supply manufacturing items to the main device (10). A method for operating such a mobile autonomous manufacturing system, in which the auxiliary devices (11,12) move autonomously to and from the main device (10) and synchronously with the main device (10) when supplying the main device (10).

A mobile robot allocation system includes an obtainer, an allocator, and an outputter. The obtainer obtains task status information regarding a task status of each of a plurality of areas, the plurality of areas each including a plurality of machines. The allocator allocates, based on the task status information obtained by the obtainer, a plurality of mobile robots to the plurality of areas, the plurality of mobile robots each moving and performing a task in an area among the plurality of areas. The outputter outputs allocation information indicating allocation of the plurality of mobile robots performed by the allocator.